Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions

Artificial intelligence (AI) technology advancements have revolutionized carotid artery research, driving innovations in diagnostics and treatment. The exponential growth of studies underscores the critical need for systematic analysis to ensure clinical relevance and algorithmic robustness.

To systematically evaluate the status of AI applications in carotid artery research, identify emerging themes and track development trends, and establish a guiding framework for future research.

A retrospective bibliometric analysis of Web of Science Core Collection data, Scopus, and PubMed databases from 2000 to 2025 was performed, focusing on title/abstract/keyword queries related to AI-driven carotid artery research. Network visualizations and structural analyses generated by CiteSpace reveal evolving thematic clusters and emerging frontiers in this interdisciplinary field.

A total of 1,220 relevant publications were identified in this study, with an overall increasing trend in publication volume. China and the United States emerged as the primary contributing countries. Research mainly focuses on early, accurate diagnosis, monitoring, individualized intervention of carotid artery disease, imaging omics analysis, and advanced physiological and pathological analysis. Recent progress is characterized by big data, multi-omics, multi-modality, and multi-scale integration, emphasizing precision medicine and full-process evaluation of disease management, marking a major shift in carotid research toward a data-driven approach.

This bibliometric study employs visualization techniques to delineate AI's transformative role in carotid artery research, demonstrating its potential to redefine research paradigms and drive sustained advancements through advanced data analytics.